def evaluate_benchmark_icp(args, test_loader):
in_dim = 6 if args.normal else 3
model = IterativeBenchmark(in_dim=in_dim, niters=args.niters, gn=args.gn)
if args.cuda:
model = model.cuda()
model.load_state_dict(torch.load(args.checkpoint))
else:
model.load_state_dict(
torch.load(args.checkpoint, map_location=torch.device('cpu')))
model.eval()
dura = []
r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic = [], [], [], [], [], []
with torch.no_grad():
for i, (ref_cloud, src_cloud, gtR,
gtt) in tqdm(enumerate(test_loader)):
if args.cuda:
ref_cloud, src_cloud, gtR, gtt = ref_cloud.cuda(), src_cloud.cuda(), \
gtR.cuda(), gtt.cuda()
tic = time.time()
R1, t1, pred_ref_cloud = model(
src_cloud.permute(0, 2, 1).contiguous(),
ref_cloud.permute(0, 2, 1).contiguous())
ref_cloud = torch.squeeze(ref_cloud).cpu().numpy()
src_cloud_tmp = torch.squeeze(pred_ref_cloud[-1]).cpu().numpy()
R2, t2, pred_ref_cloud = icp(npy2pcd(src_cloud_tmp),
npy2pcd(ref_cloud))
R2, t2 = torch.from_numpy(R2)[None, ...].to(R1), \
torch.from_numpy(t2)[None, ...].to(R1)
R, t = R2 @ R1, torch.squeeze(R2 @ t1[:, :, None], dim=-1) + t2
toc = time.time()
dura.append(toc - tic)
cur_r_mse, cur_r_mae, cur_t_mse, cur_t_mae, cur_r_isotropic, \
cur_t_isotropic = compute_metrics(R, t, gtR, gtt)
r_mse.append(cur_r_mse)
r_mae.append(cur_r_mae)
t_mse.append(cur_t_mse)
t_mae.append(cur_t_mae)
r_isotropic.append(cur_r_isotropic.cpu().detach().numpy())
t_isotropic.append(cur_t_isotropic.cpu().detach().numpy())
if args.show:
src_cloud = torch.squeeze(src_cloud).cpu().numpy()
pcd1 = npy2pcd(ref_cloud, 0)
pcd2 = npy2pcd(src_cloud, 1)
pcd3 = pred_ref_cloud
o3d.visualization.draw_geometries([pcd1, pcd2, pcd3])
r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic = \
summary_metrics(r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic)
return dura, r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic
def evaluate_benchmark(args, test_loader):
model = IterativeBenchmark(in_dim=args.in_dim,
niters=args.niters,
gn=args.gn)
if args.cuda:
model = model.cuda()
model.load_state_dict(torch.load(args.checkpoint))
else:
model.load_state_dict(torch.load(args.checkpoint, map_location=torch.device('cpu')))
model.eval()
dura = []
r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic = [], [], [], [], [], []
with torch.no_grad():
for i, (ref_cloud, src_cloud, gtR, gtt) in tqdm(enumerate(test_loader)):
if args.cuda:
ref_cloud, src_cloud, gtR, gtt = ref_cloud.cuda(), src_cloud.cuda(), \
gtR.cuda(), gtt.cuda()
tic = time.time()
R, t, pred_ref_cloud = model(src_cloud.permute(0, 2, 1).contiguous(),
ref_cloud.permute(0, 2, 1).contiguous())
toc = time.time()
dura.append(toc - tic)
cur_r_mse, cur_r_mae, cur_t_mse, cur_t_mae, cur_r_isotropic, \
cur_t_isotropic = compute_metrics(R, t, gtR, gtt)
r_mse.append(cur_r_mse)
r_mae.append(cur_r_mae)
t_mse.append(cur_t_mse)
t_mae.append(cur_t_mae)
r_isotropic.append(cur_r_isotropic.cpu().detach().numpy())
t_isotropic.append(cur_t_isotropic.cpu().detach().numpy())
if args.show:
ref_cloud = torch.squeeze(ref_cloud).cpu().numpy()
src_cloud = torch.squeeze(src_cloud).cpu().numpy()
pred_ref_cloud = torch.squeeze(pred_ref_cloud[-1]).cpu().numpy()
pcd1 = npy2pcd(ref_cloud, 0)
pcd2 = npy2pcd(src_cloud, 1)
pcd3 = npy2pcd(pred_ref_cloud, 2)
o3d.visualization.draw_geometries([pcd1, pcd2, pcd3])
r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic = \
summary_metrics(r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic)
return dura, r_mse, r_mae, t_mse, t_mae, r_isotropic, t_isotropic
def evaluate_benchmark(args, test_loader):
model = IterativeBenchmark(in_dim1=args.in_dim, niters=args.niters)
if args.cuda:
model = model.cuda()
model.load_state_dict(torch.load(args.checkpoint))
else:
model.load_state_dict(
torch.load(args.checkpoint, map_location=torch.device('cpu')))
model.eval()
dura = []
t_errors, R_errors, degree_errors = [], [], []
with torch.no_grad():
for i, (ref_cloud, src_cloud, gtR,
gtt) in tqdm(enumerate(test_loader)):
if args.cuda:
ref_cloud, src_cloud, gtR, gtt = ref_cloud.cuda(), src_cloud.cuda(), \
gtR.cuda(), gtt.cuda()
tic = time.time()
R, t, pred_ref_cloud = model(
src_cloud.permute(0, 2, 1).contiguous(),
ref_cloud.permute(0, 2, 1).contiguous())
toc = time.time()
dura.append(toc - tic)
cur_t_error = translation_error(t, -gtt)
cur_R_error = rotation_error(R, gtR.permute(0, 2, 1).contiguous())
cur_degree_error = degree_error(R,
gtR.permute(0, 2, 1).contiguous())
t_errors.append(cur_t_error.item())
R_errors.append(cur_R_error.item())
degree_errors.append(cur_degree_error.item())
if args.show:
print(cur_t_error.item(), cur_R_error.item(),
cur_degree_error.item())
ref_cloud = torch.squeeze(ref_cloud).cpu().numpy()
src_cloud = torch.squeeze(src_cloud).cpu().numpy()
pred_ref_cloud = torch.squeeze(pred_ref_cloud).cpu().numpy()
pcd1 = npy2pcd(ref_cloud, 0)
pcd2 = npy2pcd(src_cloud, 1)
pcd3 = npy2pcd(pred_ref_cloud, 2)
o3d.visualization.draw_geometries([pcd1, pcd2, pcd3])
return dura, np.mean(t_errors), np.mean(R_errors), np.mean(degree_errors)
def main():
args = config_params()
print(args)
setup_seed(args.seed)
if not os.path.exists(args.saved_path):
os.makedirs(args.saved_path)
summary_path = os.path.join(args.saved_path, 'summary')
if not os.path.exists(summary_path):
os.makedirs(summary_path)
checkpoints_path = os.path.join(args.saved_path, 'checkpoints')
if not os.path.exists(checkpoints_path):
os.makedirs(checkpoints_path)
train_set = ModelNet40(root=args.root,
npts=args.train_npts,
train=True,
normal=args.normal,
mode=args.mode)
test_set = ModelNet40(root=args.root,
npts=args.train_npts,
train=False,
normal=args.normal,
mode=args.mode)
train_loader = DataLoader(train_set,
batch_size=args.batchsize,
shuffle=True,
num_workers=args.num_workers)
test_loader = DataLoader(test_set,
batch_size=args.batchsize,
shuffle=False,
num_workers=args.num_workers)
in_dim = 6 if args.normal else 3
model = IterativeBenchmark(in_dim=in_dim, niters=args.niters, gn=args.gn)
model = model.cuda()
loss_fn = EMDLosspy().cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=args.milestones, gamma=args.gamma, last_epoch=-1)
writer = SummaryWriter(summary_path)
test_min_loss, test_min_r_mse_error, test_min_rot_error = \
float('inf'), float('inf'), float('inf')
for epoch in range(args.epoches):
print('=' * 20, epoch + 1, '=' * 20)
train_results = train_one_epoch(train_loader, model, loss_fn,
optimizer)
print_train_info(train_results)
test_results = test_one_epoch(test_loader, model, loss_fn)
print_train_info(test_results)
if epoch % args.saved_frequency == 0:
writer.add_scalar('Loss/train', train_results['loss'], epoch + 1)
writer.add_scalar('Loss/test', test_results['loss'], epoch + 1)
writer.add_scalar('RError/train', train_results['r_mse'],
epoch + 1)
writer.add_scalar('RError/test', test_results['r_mse'], epoch + 1)
writer.add_scalar('rotError/train', train_results['r_isotropic'],
epoch + 1)
writer.add_scalar('rotError/test', test_results['r_isotropic'],
epoch + 1)
writer.add_scalar('Lr', optimizer.param_groups[0]['lr'], epoch + 1)
test_loss, test_r_error, test_rot_error = \
test_results['loss'], test_results['r_mse'], test_results['r_isotropic']
if test_loss < test_min_loss:
saved_path = os.path.join(checkpoints_path, "test_min_loss.pth")
torch.save(model.state_dict(), saved_path)
test_min_loss = test_loss
if test_r_error < test_min_r_mse_error:
saved_path = os.path.join(checkpoints_path,
"test_min_rmse_error.pth")
torch.save(model.state_dict(), saved_path)
test_min_r_mse_error = test_r_error
if test_rot_error < test_min_rot_error:
saved_path = os.path.join(checkpoints_path,
"test_min_rot_error.pth")
torch.save(model.state_dict(), saved_path)
test_min_rot_error = test_rot_error
scheduler.step()
def main():
args = config_params()
print(args)
setup_seed(args.seed)
if not os.path.exists(args.saved_path):
os.makedirs(args.saved_path)
summary_path = os.path.join(args.saved_path, 'summary')
if not os.path.exists(summary_path):
os.makedirs(summary_path)
checkpoints_path = os.path.join(args.saved_path, 'checkpoints')
if not os.path.exists(checkpoints_path):
os.makedirs(checkpoints_path)
train_set = ModelNet40(args.root, args.train_npts)
test_set = ModelNet40(args.root, args.train_npts, False)
train_loader = DataLoader(train_set,
batch_size=args.batchsize,
shuffle=True,
num_workers=args.num_workers)
test_loader = DataLoader(test_set,
batch_size=args.batchsize,
shuffle=False,
num_workers=args.num_workers)
model = IterativeBenchmark(in_dim1=args.in_dim, niters=args.niters)
model = model.cuda()
loss_fn = EMDLosspy()
loss_fn = loss_fn.cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=args.milestones, gamma=args.gamma, last_epoch=-1)
writer = SummaryWriter(summary_path)
test_min_loss, test_min_t_error, test_min_R_error, test_min_degree_error = \
float('inf'), float('inf'), float('inf'), float('inf')
for epoch in range(args.epoches):
print('=' * 20, epoch + 1, '=' * 20)
train_loss, train_t_error, train_R_error, train_degree_error = \
train_one_epoch(train_loader, model, loss_fn, optimizer)
print(
'Train: loss: {:.4f}, t_error: {:.4f}, R_error: {:.4f}, degree_error: {:.4f}'
.format(train_loss, train_t_error, train_R_error,
train_degree_error))
test_loss, test_t_error, test_R_error, test_degree_error = \
test_one_epoch(test_loader, model, loss_fn)
print(
'Test: loss: {:.4f}, t_error: {:.4f}, R_error: {:.4f}, degree_error: {:.4f}'
.format(test_loss, test_t_error, test_R_error, test_degree_error))
if epoch % args.saved_frequency == 0:
writer.add_scalar('Loss/train', train_loss, epoch + 1)
writer.add_scalar('Loss/test', test_loss, epoch + 1)
writer.add_scalar('TError/train', train_t_error, epoch + 1)
writer.add_scalar('TError/test', test_t_error, epoch + 1)
writer.add_scalar('RError/train', train_R_error, epoch + 1)
writer.add_scalar('RError/test', test_R_error, epoch + 1)
writer.add_scalar('degreeError/train', train_degree_error,
epoch + 1)
writer.add_scalar('degreeError/test', test_degree_error, epoch + 1)
writer.add_scalar('Lr', optimizer.param_groups[0]['lr'], epoch + 1)
if test_loss < test_min_loss:
saved_path = os.path.join(checkpoints_path, "test_min_loss.pth")
torch.save(model.state_dict(), saved_path)
test_min_loss = test_loss
if test_t_error < test_min_t_error:
saved_path = os.path.join(checkpoints_path, "test_min_t_error.pth")
torch.save(model.state_dict(), saved_path)
test_min_t_error = test_t_error
if test_R_error < test_min_R_error:
saved_path = os.path.join(checkpoints_path, "test_min_R_error.pth")
torch.save(model.state_dict(), saved_path)
test_min_R_error = test_R_error
if test_degree_error < test_min_degree_error:
saved_path = os.path.join(checkpoints_path,
"test_min_degree_error.pth")
torch.save(model.state_dict(), saved_path)
test_min_degree_error = test_degree_error
scheduler.step()